Re SVG: and it would likely strip any SynthID watermarks.

It could be viable to encode SynthID-like variables in subtle variations of positioning of elements, path lengths, etc. I think I saw an article on here a couple weeks ago which used such techniques to hide 'messages' in SVGs. It might even be possible to do it in a way which survives basic manipulations, as with rasters.

Assume I'm naive on this matter, but why wouldn't Nvidia allow its own DGX OS to be a first party OS on these devices? Some sort of MS subsidy? Ecosystem-wise, wouldn't it make sense for Nvidia to keep pushing its own OS on all the devices containing its hardware?

I don't really have a definitive answer, so this is more thinking out loud, but why do the Economist, Spectator, WSJ, NYT, or the like continue to do reasonably well?

News is everywhere now, and AI can do a reasonable job guiding me on any economic or political topic I might care about. Yet.. named authors with track records or strong personalities/opinions still seem to draw readers and have value, and maybe CSS Tricks is mildly like that in some niches.

I say this with a slight tongue in cheek, but Mistral does have a better option: they recently added GLM 5.3 to their platform (and it works rather well). Not trained in the EU, though, of course ;-)

Yeah, absolutely true. And I am guilty of simply using Mistral as a GLM subscription myself. I just really like the GLM 5.3 model.

I will see where Chonk is better though, since you have both options in one subscription.

As someone who was intrigued by these new machines but also has no interest in running Windows, I'm quite happy to learn it works. Though admittedly I was expecting to see DGX OS first.

To be fair, Deno and Bun's enhancements seemed to do a lot to set a fire under Node and have it implement various things it didn't have when both projects launched. This is a good thing.

A bit frivolous, but I once used a spare VPS like that as a one-listener Internet radio station for my mom which worked with her portable radio (she has no interest in using Spotify etc.)

Smaller models have been able to do these sorts of tasks, but a little slower, for a while now. Give a small Qwen 3.8 model a classification task and force a structured output, and it'll do a good job. I've used Qwen 0.8b for basic image classification in <500ms on my local machine for a while now.

There are a few technical details that can reduce the latency significantly (covered in the post) but the real insight has been from watching the reaction to Jev and seeing that there's enough of a market interest to offer it as a distinct thing. The underlying concept/approach was already there.

Not just structured output. Dropping down to logprobs, prompting the model to emit one word as the answer, and then ranking the output tokens to pick your answer works great on small Qwen & Gemma models.

The fascinating part to me is that Jev seems like this technique plus post-training to get multiple independent confidence values for each possible answer.

You can also go beyond Jev. Qwen 3.5 0.8B is fantastic at basic image classification/question answering (including OCR elements) also. Though rather than looking at logits, I get it to output a structured JSON object and it does simple object classification tasks on a Mac at under 500ms a pop (I forget how far, but I think it's like ~250ms) with good accuracy (depending on task).

I had a similar thing with Xcode the other day. I unticked the "download a code assistance model" thing, clicked to continue and it started downloading it anyway (which, to be fair, you can just X out of).